This work aimed to offer a non-destructive and fast approach to visualizing the soluble solids content (SSC) and acidity (pH) of the whole kiwifruit. Most of the visible-near-infrared spectral imaging techniques used in postharvest fruit and vegetables assessment exhibit issues related to the identification of the quality spatial distribution within intact samples, mainly due to sampling surface curvature effects. Here, a push-broom-type NIR hyperspectral imaging camera and a sample rotation stage were combined to scan entire kiwifruit surfaces. Then, key wavelengths in the range of 1002–2300 nm were extracted for constructing SSC and pH calibration models by partial least squares regression analysis. The resulting SSC prediction accuracy was sufficiently high: the coefficient of determination (R²_cv) and the root mean square error (RMSE_cv) of cross-validation set were 0.74 and 0.7 %, respectively. For pH, the R²_cv and RMSE_cv were 0.64 and 0.14, respectively. Finally, the SSC and pH 360˚mapping results surpassed earlier works in this area that they showed a distinct spatial distribution within each intact sample. It was concluded that the proposed object rotation hyperspectral imaging approach is promising for the non-destructive prediction mapping of SSC and pH in kiwifruit or other cylindrical-shaped samples.

这项工作旨在提供一种非破坏性的快速方法来可视化整个奇异果的可溶性固形物含量（SSC）和酸度（pH）。收获后水果和蔬菜评估中使用的大多数可见-近红外光谱成像技术都表现出与完整样本内质量空间分布的识别有关的问题，这主要归因于采样表面曲率效应。在这里，结合了推扫式近红外高光谱成像相机和样品旋转台，以扫描整个奇异果表面。然后，通过偏最小二乘回归分析，提取1002–2300 nm范围内的关键波长，以构建SSC和pH校准模型。所得的SSC预测精度足够高：确定系数（R ²交叉验证集的_cv）和均方根误差（RMSE _cv）分别为0.74和0.7％。对于pH，R ² _cv和RMSE _cv分别为0.64和0.14。最终，SSC和pH 360映射结果超过了该区域的早期工作，它们在每个完整样品中均表现出独特的空间分布。结论是，提出的目标旋转高光谱成像方法有望用于奇异果或其他圆柱状样品中SSC和pH值的无损预测。